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  <h1>Source code for nlp_architect.nn.tensorflow.python.keras.layers.crf</h1><div class="highlight"><pre>
<span></span><span class="c1"># ******************************************************************************</span>
<span class="c1"># Copyright 2017-2018 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1"># ******************************************************************************</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>


<div class="viewcode-block" id="CRF"><a class="viewcode-back" href="../../../../../../../generated_api/nlp_architect.nn.tensorflow.python.keras.layers.html#nlp_architect.nn.tensorflow.python.keras.layers.crf.CRF">[docs]</a><span class="k">class</span> <span class="nc">CRF</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Layer</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Conditional Random Field layer (tf.keras)</span>
<span class="sd">    `CRF` can be used as the last layer in a network (as a classifier). Input shape (features)</span>
<span class="sd">    must be equal to the number of classes the CRF can predict (a linear layer is recommended).</span>

<span class="sd">    Note: the loss and accuracy functions of networks using `CRF` must</span>
<span class="sd">    use the provided loss and accuracy functions (denoted as loss and viterbi_accuracy)</span>
<span class="sd">    as the classification of sequences are used with the layers internal weights.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_labels (int): the number of labels to tag each temporal input.</span>

<span class="sd">    Input shape:</span>
<span class="sd">        nD tensor with shape `(batch_size, sentence length, num_classes)`.</span>

<span class="sd">    Output shape:</span>
<span class="sd">        nD tensor with shape: `(batch_size, sentence length, num_classes)`.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">CRF</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="c1"># num of output labels</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_classes</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_spec</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">min_ndim</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">supports_masking</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sequence_lengths</span> <span class="o">=</span> <span class="kc">None</span>

<div class="viewcode-block" id="CRF.get_config"><a class="viewcode-back" href="../../../../../../../generated_api/nlp_architect.nn.tensorflow.python.keras.layers.html#nlp_architect.nn.tensorflow.python.keras.layers.crf.CRF.get_config">[docs]</a>    <span class="k">def</span> <span class="nf">get_config</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">config</span> <span class="o">=</span> <span class="p">{</span>
            <span class="s2">&quot;output_dim&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">,</span>
            <span class="s2">&quot;supports_masking&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">supports_masking</span><span class="p">,</span>
            <span class="s2">&quot;transitions&quot;</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">),</span>
        <span class="p">}</span>
        <span class="n">base_config</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">CRF</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">get_config</span><span class="p">()</span>
        <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">base_config</span><span class="o">.</span><span class="n">items</span><span class="p">())</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">config</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span></div>

<div class="viewcode-block" id="CRF.build"><a class="viewcode-back" href="../../../../../../../generated_api/nlp_architect.nn.tensorflow.python.keras.layers.html#nlp_architect.nn.tensorflow.python.keras.layers.crf.CRF.build">[docs]</a>    <span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span>
        <span class="n">f_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">TensorShape</span><span class="p">(</span><span class="n">input_shape</span><span class="p">)</span>
        <span class="n">input_spec</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">InputSpec</span><span class="p">(</span><span class="n">min_ndim</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">{</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span> <span class="n">f_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]})</span>

        <span class="k">if</span> <span class="n">f_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;The last dimension of the inputs to `CRF` &quot;</span> <span class="s2">&quot;should be defined. Found `None`.&quot;</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="n">f_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;The last dimension of the input shape must be equal to output&quot;</span>
                <span class="s2">&quot; shape. Use a linear layer if needed.&quot;</span>
            <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">input_spec</span> <span class="o">=</span> <span class="n">input_spec</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_weight</span><span class="p">(</span>
            <span class="n">name</span><span class="o">=</span><span class="s2">&quot;transitions&quot;</span><span class="p">,</span>
            <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">],</span>
            <span class="n">initializer</span><span class="o">=</span><span class="s2">&quot;glorot_uniform&quot;</span><span class="p">,</span>
            <span class="n">trainable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">built</span> <span class="o">=</span> <span class="kc">True</span></div>

    <span class="c1"># pylint: disable=arguments-differ</span>
<div class="viewcode-block" id="CRF.call"><a class="viewcode-back" href="../../../../../../../generated_api/nlp_architect.nn.tensorflow.python.keras.layers.html#nlp_architect.nn.tensorflow.python.keras.layers.crf.CRF.call">[docs]</a>    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">sequence_lengths</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="n">sequences</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">convert_to_tensor</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">sequence_lengths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sequence_lengths</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span>
            <span class="k">assert</span> <span class="n">tf</span><span class="o">.</span><span class="n">convert_to_tensor</span><span class="p">(</span><span class="n">sequence_lengths</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="s2">&quot;int32&quot;</span>
            <span class="n">seq_len_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">convert_to_tensor</span><span class="p">(</span><span class="n">sequence_lengths</span><span class="p">)</span><span class="o">.</span><span class="n">get_shape</span><span class="p">()</span><span class="o">.</span><span class="n">as_list</span><span class="p">()</span>
            <span class="k">assert</span> <span class="n">seq_len_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">sequence_lengths</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">sequence_lengths</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">sequence_lengths</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">inputs</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span>
                <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">inputs</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
            <span class="p">)</span>

        <span class="n">viterbi_sequence</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">crf</span><span class="o">.</span><span class="n">crf_decode</span><span class="p">(</span>
            <span class="n">sequences</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sequence_lengths</span>
        <span class="p">)</span>
        <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">viterbi_sequence</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">in_train_phase</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span></div>

<div class="viewcode-block" id="CRF.loss"><a class="viewcode-back" href="../../../../../../../generated_api/nlp_architect.nn.tensorflow.python.keras.layers.html#nlp_architect.nn.tensorflow.python.keras.layers.crf.CRF.loss">[docs]</a>    <span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
        <span class="n">y_pred</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">convert_to_tensor</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
        <span class="n">log_likelihood</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">crf</span><span class="o">.</span><span class="n">crf_log_likelihood</span><span class="p">(</span>
            <span class="n">y_pred</span><span class="p">,</span>
            <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_true</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">sequence_lengths</span><span class="p">,</span>
            <span class="n">transition_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="o">-</span><span class="n">log_likelihood</span><span class="p">)</span></div>

<div class="viewcode-block" id="CRF.compute_output_shape"><a class="viewcode-back" href="../../../../../../../generated_api/nlp_architect.nn.tensorflow.python.keras.layers.html#nlp_architect.nn.tensorflow.python.keras.layers.crf.CRF.compute_output_shape">[docs]</a>    <span class="k">def</span> <span class="nf">compute_output_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">):</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">TensorShape</span><span class="p">(</span><span class="n">input_shape</span><span class="p">)</span><span class="o">.</span><span class="n">assert_has_rank</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">input_shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">,)</span></div>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">viterbi_accuracy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">def</span> <span class="nf">accuracy</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
            <span class="n">shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span>
            <span class="n">sequence_lengths</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
            <span class="n">viterbi_sequence</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">crf</span><span class="o">.</span><span class="n">crf_decode</span><span class="p">(</span>
                <span class="n">y_pred</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">,</span> <span class="n">sequence_lengths</span>
            <span class="p">)</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">viterbi_sequence</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span><span class="p">)</span>
            <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">categorical_accuracy</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span>

        <span class="n">accuracy</span><span class="o">.</span><span class="n">func_name</span> <span class="o">=</span> <span class="s2">&quot;viterbi_accuracy&quot;</span>
        <span class="k">return</span> <span class="n">accuracy</span></div>
</pre></div>

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